Data Preparation

Model your data as a series of process observations or measures that are associated with an outcome of interest.  Compose each observation as a common set of features (aka., independent variables or factors) along with the associated outcome.  Both features and outcomes are either numerical (dates, times, ages, etc.), binary (yes/no, true/false, etc.) or categorical (Gender, Service line , Floor unit, Shift, DRG, etc.).


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Submission & Processing

Transfer data via a secure dedicated sFTP account or use a local application

  1. Our automation process will email you when data is received by our sFTP server in a HIPAA compliant HITRUST environment.
  2. Your data is automatically routed into a preprocessing stage which notifies you of errors which would stop further processing.
  3. Near real-time outcome prediction is an option using network transfers or applications running on your network.  Internet access is not required for predictions.


  1. We engineer features, as needed, to remove noise and improve the quality of findings and predictive models.
  2. Our machine learning engine (MLE) pairs supervised learning methods with an unsupervised learning pre-training stage to remove noise and improve predictive performance.
  3. The MLE associates patterns in the data with outcomes of interest (data mining)
  4. The MLE tests multiple supervised learning algorithms, including deep learning, to choose the most cost effective predictive model for the business goal.
  5. We use both multi-fold cross validation and separate sample testing to measure and verify the predictivity and accuracy in the predictive models.

 Automated Decision Rule Generation Provides Quick Insight

  1. Automatic generation of simple predictive decisioning rules allows you to easily distinguish factors associated with outcomes of interest, extract actionable factors and design corrective process interventions
  2. We periodically retrain and test the predictive models to provide ongoing predictions based on fresh learning
  3. Confidence scores accompany predictions so that interventions my be prioritized within higher risk groups.



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